{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris=pd.read_csv(\"Iris_Data.csv\")\n",
    "iris=pd.DataFrame(iris)\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>abbrev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species  abbrev\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa  setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa  setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa  setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa  setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa  setosa"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['abbrev']=iris.species.apply(lambda x: x.replace('Iris-',''))\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "abbrev          150 non-null object\n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 7.1+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x78d445c940>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='species',data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x78d2d0e0b8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6624x288 with 23 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='abbrev',col='sepal_width',kind='count',data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x78d53d0e48>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6336x288 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='abbrev',col='petal_width',kind='count',data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x78d71b3a58>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter('abbrev','sepal_width','petal_width',data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x78d71b44e0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    7.9\n",
       "sepal_width     4.4\n",
       "petal_length    6.9\n",
       "petal_width     2.5\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.quantile(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x78d7257c50>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=iris.corr()\n",
    "sns.heatmap(corr,xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x78d72dcb38>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='abbrev',y='petal_width',data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x78d7325908>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.5x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='petal_length',y='petal_width',hue='species',fit_reg=False,data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x78d7257d30>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='petal_length',y='petal_width',col='species',hue='species',fit_reg=False,data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.drop(columns=['abbrev'],inplace=True)\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Iris-virginica', 'Iris-versicolor', 'Iris-setosa'}\n"
     ]
    }
   ],
   "source": [
    "print(set(iris['species']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(iris)):\n",
    "    if(iris['species'][i]=='Iris-virginica'):\n",
    "        iris['species'][i]=0\n",
    "    elif(iris['species'][i]=='Iris-versicolor'):\n",
    "        iris['species'][i]=1\n",
    "    else:\n",
    "        iris['species'][i]=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2}\n"
     ]
    }
   ],
   "source": [
    "print(set(iris['species']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 5)\n",
      "(50, 5)\n",
      "(100,)\n",
      "(50,)\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn import cross_validation\n",
    "X_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(\n",
    "iris, iris.species, test_size=0.33)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm.fit(X_train,Y_train)\n",
    "pred_train=lm.predict(X_train)\n",
    "pred_test=lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1)\n"
     ]
    }
   ],
   "source": [
    "pred_train=pred_train.reshape((100,1))\n",
    "print(pred_train.shape)\n",
    "#Y_train.reshape([100,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1)\n"
     ]
    }
   ],
   "source": [
    "Y_train=np.array(Y_train)\n",
    "Y_train=Y_train.reshape((100,1))\n",
    "print(Y_train.shape)\n",
    "#Y_train.reshape([100,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train=pred_train.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.0\n"
     ]
    }
   ],
   "source": [
    "sumcheck=0\n",
    "for i in range(0,len(pred_train)):\n",
    "    error=Y_train[i][0]-pred_train[i][0]\n",
    "    error=error*error\n",
    "    sumcheck=sumcheck+error\n",
    "print(sumcheck/len(pred_train)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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